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From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
reL Re: st: Interpreting mediation model sobel goodman test |

Date |
Thu, 10 Nov 2011 10:56:04 -0500 |

Hi John, While this is a relatively old thread (in statalist time a month is like a century), I am revisiting your code below and have a question. In your -reg3- equation and subsequent nlcom, you recover the "total effect". How would you recover the direct and indirect effects using -reg3-? In a separate set of postings dated Feb 2009, Maarten laid out an approach using -sureg-, but it doesn't appear that the thread ever came back to -reg3- . The primary issue here is that one would need to have an outcome model containing both the mediator (m) and treatment variable (x), in order to derive the direct effect of x on y. The -reg3- model below for the outcome does not contain the x variable (x is treated as exogenous). Thanks Ariel From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of John Antonakis Sent: Tuesday, October 18, 2011 12:11 PM To: statalist@hsphsun2.harvard.edu Subject: Re: st: Interpreting mediation model sobel goodman test Hi Meredith: I assume you used the -sgmediation- package; I would not use this routine UNLESS your mediator is exogenous (and you are sure of this). If it is endogenous sgmedation will give you inconsistent estimates (it estimates the system of equations with OLS, and uses the dated Baron-Kenny methods); you do not tackle the endogeneity problem with sgmediation. You need to estimate your system of equations with an instrumental-variable estimator (e.g., 2SLS). Take a look at this podcast, where I discuss this problem in detail: Endogeneity: An inconvenient truth (full version) (about 32 minutes in length) http://www.youtube.com/watch?v=dLuTjoYmfXs If you just want the nitty gritty see: Endogeneity: An inconvenient truth (for researchers) (Excludes the "gentle introduction" content and discusses the two-stage least squares estimator straight away; about 16 minutes in length) http://www.youtube.com/watch?v=yi_5M7oUceE See also: Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (submitted). Causality and endogeneity: Problems and solutions. In D.V. Day (Ed.), The Oxford Handbook of Leadership and Organizations. http://www.hec.unil.ch/jantonakis/Causality_and_endogeneity_final.pdf To understand exactly the nature of the problem run the following code, where x is endogenous with respect to y: clear set seed 123 set obs 1000 gen x = rnormal() gen e = rnormal() gen m = e + .5*x + rnormal() gen y = .5*m - e + rnormal() reg3 (y = m) (m = x), 2sls nlcom [m]x*[y]m sgmediation y, mv(m) iv(x) From the above model, we have an instrument x, an endogenous regressor m, and omitted cause e, and a dependent variable y. We know that the indirect effect of x on y is .5*.5=.25. 2SLS recovers this parameter well (.24, p>.001). However, the sgmediation program gives .03 (and p = .04). Now, let's rerun this to see when you'd get the same results with sgmediation (if x is exogenous with respect to y): clear set seed 123 set obs 1000 gen x = rnormal() gen e = rnormal() gen m = .5*x + rnormal() gen y = .5*m + rnormal() reg3 (y = m) (m = x), 2sls nlcom [m]x*[y]m reg3 (y = m) (m = x), ols nlcom [m]x*[y]m sgmediation y, mv(m) iv(x) Notice that the 2SLS model is still consistent (but less efficient). The OLS estimator and sgmediation pretty much give the same estimates and standard errors. HTH, John. __________________________________________ Prof. John Antonakis Faculty of Business and Economics Department of Organizational Behavior University of Lausanne Internef #618 CH-1015 Lausanne-Dorigny Switzerland Tel ++41 (0)21 692-3438 Fax ++41 (0)21 692-3305 http://www.hec.unil.ch/people/jantonakis Associate Editor The Leadership Quarterly __________________________________________ On 18.10.2011 19:41, Meredith T. Niles wrote: > Hello all, > I am working on running multiple and single mediation models to assess > farmer climate change perceptions and potential adoption of climate > change practices. I am getting an odd result when running a Sobel > goodman test in Stata with regards to the portion of total effect that > is mediated (5.139). Does anyone have any perspective on why this > number is so large? Running the same test with another set of climate > change practices yields a proportion of total effect that is mediated at > 0.79 which seems much more in line with other results I've seen. > > > Sobel-Goodman Mediation Tests > > Coef Std Err Z P>|Z| > Sobel -.09959383 .05075882 -1.962 .04975096 > Goodman-1 -.09959383 .05217108 -1.909 .05626401 > Goodman-2 -.09959383 .04930612 -2.02 .04339293 > > Indirect effect = -.09959383 > Direct effect = .08021537 > Total effect = -.01937846 > > Proportion of total effect that is mediated: 5.1394091 > Ratio of indirect to direct effect: -1.2415804 > > > Thanks for your thoughts. > > Best, > Meredith Niles > > > PhD Candidate, Graduate Group in Ecology > NSF REACH IGERT Trainee > Deputy External Chair, Graduate Student Association > University of California, Davis > 2126 Wickson * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: reL Re: st: Interpreting mediation model sobel goodman test***From:*John Antonakis <John.Antonakis@unil.ch>

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